The present systems and methods relate to devices, methods and systems for detecting disorders of, and positively affecting the functioning of living tissues such as the brain and spinal cord.
The successful development of new clinical concepts and interventions for neurological diseases of the brain require, first and foremost, a strong theoretical framework for understanding healthy brain function and the brain's capacity for intelligent action. Unfortunately, attempts to understand and explain brain function and dysfunction have been fragmented into several narrow fields of study. In order to study brain function, some researchers (for example, see www.alleninstitute.org) have attempted to reverse-engineer neuronal networks and even the brain itself. This approach was based on the assumption that neurons in-vivo acted just like simple transistors in-silico. Unfortunately, both network and whole-brain modeling have led to very little insight into actual brain function. This is largely because transistor-based computing reacts to static events whilst neurons can react to processes. In contrast to transistors, neurons can establish and change their connections and vary their signaling properties according to a variety of rules, allowing them to adapt to circumstances, self-assemble, auto-calibrate and store information by changing their properties according to experience (Laughlin & Sejnowski, 2003). Consequently, a detailed understanding of neuronal function and network organization is required prior to neuronal network modeling attempts.
Block (1962) describes the “perceptron,” or a series of sensory and associator units connected to resemble sensory and analytical components into a machine that vaguely models human response to sensory stimuli. Stimuli of a certain threshold trigger activity in specific associator units, which then activate those to which they are directly connected. Thus, different types of stimuli activate different networks of associator components. In this sense, Block's perceptron approach to modeling brain function privileges the connections between components rather than the components themselves as the primarily important in decoding human thought (Block 1962). However, there still remains the question of what constitutes a basic unit of connectivity. Does a single connection between two associates constitute a fundamental unit of perceptron “thought?” Studying the structure and function of different types of neural connections promises significant contributions, but this still doesn't answer the question of whether these connections constitute a “thought”.
Lamb (2010) introduces the concept of the functional web, in which he posits that cognitive concepts such as single words and ideas (analogous to semantic primitives) are in fact spatially distributed across parts of the brain such as the cerebral cortex. Lamb splits these concepts into conceptual, motor, phonological image, tactile, and visual components, or components that roughly align with the senses. This approach not only applies to cognition but also to the concepts that comprise it, and is intuitive since its criteria are empirically grounded. In addition, it unifies behavioral and linguistic activity with neurological activity. Lamb's approach is more focused on response and activation, but the nature of cognition is such that thought can beget more thought; an external agent is not consistently necessary. Tying cognition not just to specific sensory activity but also to brain activity in itself is also a requirement for successful modeling.
Blais et al. (2000) argue that modeling cognitive activity based on synaptic modification depends in large part on how synapses are stabilized after firing. With respect to synaptic activity, there are numerous types of “learning,” each of which has a different neuronal effect. Hebbian learning, for instance, occurs when the connectivity between two neurons increases after one produces an action potential in the other. The selectivity-learning rule, on the other hand, incorporates a variable threshold of activation because it modulates the type and level of response to sensory stimuli (for instance, the difference between looking at the sun or at the night sky).
Blais et al. demonstrate an important mathematical connection between biology and temporality, or the idea that modeling such processes as cognition involves the accounting for change rather than for absolute physical values, and in doing so demonstrates the process parallelism that pervades natural phenomena.
There is a need for a new class of brain diagnostics and therapeutic devices. There is a need to unify the “read” and “write” aspects of clinical neuroscience. There is a need for detecting disorders of and positively affecting the functioning of living tissues such as the brain and spinal cord.